Computational Molecular Biology 2024, Vol.14, No.2, 64-75 http://bioscipublisher.com/index.php/cmb 64 Feature Review Open Access Emerging Trends in Multi-Omics Data Integration: Challenges and Future Directions JieZhang Institute of Life Sciences, Jiyang Colloge of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: jie.zhang@jicat.org Computational Molecular Biology, 2024, Vol.14, No.2 doi: 10.5376/cmb.2024.14.0008 Received: 09 Feb., 2024 Accepted: 20 Mar., 2024 Published: 07 Apr., 2024 Copyright © 2024 Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang J., 2024, Emerging Trends in multi-omics data integration: challenges and future directions, Computational Molecular Biology, 14(2): 64-75 (doi: 10.5376/cmb.2024.14.0008) Abstract This study analyzed the latest trends, challenges, and future directions of multi omics data integration. High throughput technology enables the generation of large amounts of data at multiple omics levels, including genomics, transcriptomics, proteomics, and metabolomics. However, integrating these heterogeneous datasets faces significant challenges due to differences in data types, dimensions, and a lack of standardized analysis protocols. We discussed various integration methods, including data-driven, knowledge driven, and machine learning approaches, with a focus on their applications in disease subtype classification, biomarker discovery, and precision medicine. In addition, we also analyzed the computational and visualization challenges associated with single-cell multi omics data and proposed future directions for developing stronger and more interpretable integration strategies, hoping to provide a comprehensive overview of the current status of multi omics data integration and demonstrate its potential in translational biomedical research and clinical practice. Keywords Multi-omics integration; High-throughput technologies; Machine learning; Precision medicine; Single-cell analysis 1 Introduction The advent of high-throughput technologies has revolutionized the field of biological research, enabling the comprehensive profiling of various molecular layers within biological systems. These layers include genomics, transcriptomics, proteomics, metabolomics, and more recently, single-cell omics (Misra et al., 2019; Miao et al., 2021; Wörheide et al., 2021). Multi-omics approaches aim to integrate these diverse datasets to provide a holistic view of the complex molecular interactions and regulatory mechanisms that underpin biological processes and disease states (Ebrahim et al., 2016; Colomé-Tatché and Theis, 2018; Wörheide et al., 2021). The integration of multi-omics data allows researchers to uncover hidden biological regularities and gain deeper insights into cellular functions and physiological responses (Ebrahim et al., 2016; Santiago-Rodriguez and Hollister, 2021). The integration of multi-omics data is crucial for advancing our understanding of complex biological systems. By combining data from different omics layers, researchers can achieve a more comprehensive and nuanced understanding of the molecular underpinnings of health and disease (Misra et al., 2019; Wörheide et al., 2021; Agamah et al., 2022). This integrative approach facilitates the identification of novel biomarkers, disease subtypes, and therapeutic targets, thereby enhancing the precision and efficacy of medical interventions (Graw et al., 2020; Santiago-Rodriguez and Hollister, 2021). Moreover, multi-omics data integration helps in overcoming the limitations of individual omics datasets, such as data heterogeneity and high dimensionality, by providing a more robust and contextually relevant analysis (Misra et al., 2019; Sokač et al., 2023). This study provides a comprehensive overview of emerging trends in multi omics data integration, with a focus on the current challenges and future directions in this rapidly developing field. Researchers discuss various integration methods, including data-driven, knowledge-based, simultaneous, and step-by-step approaches, and their applications in recent multi omics studies. In addition, computational and statistical tools developed for the integration of multi omics data will be explored, emphasizing their advantages, limitations, and potential for standardization.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==